Release Notes v2.1

VPI-2.1.16

VPI-2.1.16 is the first production release of VPI-2.1 branch. It contains several new algorithms and bug fixes.

Release Highlights

• New algorithms:
  • Median Filter for both CPU and CUDA backends.
  • FAST Corners Detector for both CPU and CUDA backends.
  • Mix Channels for both CPU and CUDA backends.
• KLT Feature Tracker
  • Added back the PVA implementation, now with better performance and quality.
• Gaussian Pyramid Generator
  • Added support for U8, S8 and S16 image formats to PVA backend implementation.

Selected Bug Fixes

• vpiWarpMapAllocData now allocates less memory.

Other C API updates

• Added vpiInitFASTCornerDetectorParams and vpiSubmitFASTCornerDetector .
• Added vpiSubmitMedianFilter .
• Added vpiSubmitMixChannels .
• Added VPI_ARRAY_TYPE_F32 for creating arrays, together with its python counterpart, vpi.Type.F32.
• Added VPI_ARRAY_TYPE_U32 and the corresponding VPIKeypointU32 struct.
• Added VPI_ARRAY_TYPE_KEYPOINT_UQ1616, for fixed-point UQ16.16 elements, to be used with the new VPIKeypointU32 struct.
• Added vpiImageFormatGetPlaneWidth and vpiImageFormatGetPlaneHeight .
• Added vpiPixelTypeGetChannelType .
• Rename VPIKeypoint to VPIKeypointF32
• Rename VPI_ARRAY_TYPE_KEYPOINT to VPI_ARRAY_TYPE_KEYPOINT_F32

Python API updates

• Added Median Filter .
• Added FAST Corners Detector .
• Added Mix Channels .
• Added __version__ to VPI python module.
• Added vpi.Type.KEYPOINT_F32, making the existing vpi.Type.KEYPOINT an alias to it.
• Added vpi.KeypointF32, making the existing vpi.Keypoint an alias to it.

Known Issues

• Host images wrapped into VPIImages using vpiImageCreateWrapper might incur in a performance hit when using them with algorithms running on CUDA backend. User should avoid wrappers in this case, preferring to use VPIImages allocated with vpiImageCreate.
• Possible performance hit when using CUDA images wrapped into VPIImages using vpiImageCreateWrapper in algorithms running in PVA, VIC and/or NVENC. User should avoid using wrappers in this case, preferring to use VPIImages allocated with vpiImageCreate.
• Harris Corner Detector result scores/positions might differ among backends.
• PyTorch/CUDA interoperability might not work on Tegra due to some issues with CUDA support with PyTorch in this platform and how it behaves when VPI module is loaded.
• Stereo Disparity Estimator
  • output differs significantly between CPU and new CUDA backend implementation.
  • On CPU backend, no checking on maximum disparity limit is being performed. It's recommended set maximum disparity to at most 64. Using a higher value leads to undefined behavior: too much memory is allocated, which may lead to system running out of memory.
  • The confidence map generated by OFA+PVA+VIC backend might have some negligible differences with respect to other backends.
• Remap and Stereo demo applications
  • On Tegra platforms, the demos might fail to connect to the device's camera and segfault.
• Performance hit using Dense Optical Flow on python due re-creating the payload at every call.
• Per-algorithm performance tables weren't updated, they still refer to performance from vpi-2.0.

• Attention

  Algorithms running on PVA backend won't work inside a docker container. Submission calls will return error VPI_ERROR_INTERNAL .

Notices

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